Business Intelligence (BI) computing system users range from executives to data enthusiasts who share a common way of interaction: they navigate large datasets by means of sequences of analytical queries elaborated through user-friendly interfaces. For example, users may express their information needs via keywords, and let the system infer from them the most probable formal queries (generally MDX or SQL) to be send to the underlying data sources (generally data warehouses or databases). As information needs do not have a status per se, it usually takes many interactions with the system to satisfy an information need, and the overall session is often a tedious process, especially in the case when the information need is not even clear for the user. This bears resemblance with web search where users typically need to repeatedly query the search engine to determine whether there is interesting content.
Being able to automatically identify user interests from BI interactions is a challenging problem that has many potential applications: collaborative recommendation (of data or dashboards), repetitive task prediction, alert raising, etc. therefore reducing the tediousness of the analysis. The difficulty of this problem lies in the fact that user interests are hidden in the interactions, and two users with the same interest would probably interact with the system differently. As in web search where users may have no idea of the retrieval algorithm, BI user are generally ignorant of the data sources and the formal queries they trigger.